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. 2021 Mar 29:12:641713.
doi: 10.3389/fendo.2021.641713. eCollection 2021.

An Analysis of Glucose Effectiveness in Subjects With or Without Type 2 Diabetes via Hierarchical Modeling

Affiliations

An Analysis of Glucose Effectiveness in Subjects With or Without Type 2 Diabetes via Hierarchical Modeling

Shihao Hu et al. Front Endocrinol (Lausanne). .

Abstract

Glucose effectiveness, defined as the ability of glucose itself to increase glucose utilization and inhibit hepatic glucose production, is an important mechanism maintaining normoglycemia. We conducted a minimal modeling analysis of glucose effectiveness at zero insulin (GEZI) using intravenous glucose tolerance test data from subjects with type 2 diabetes (T2D, n=154) and non-diabetic (ND) subjects (n=343). A hierarchical statistical analysis was performed, which provided a formal mechanism for pooling the data from all study subjects, to yield a single composite population model that quantifies the role of subject specific characteristics such as weight, height, age, sex, and glucose tolerance. Based on the resulting composite population model, GEZI was reduced from 0.021 min-1 (standard error - 0.00078 min-1) in the ND population to 0.011 min-1 (standard error - 0.00045 min-1) in T2D. The resulting model was also employed to calculate the proportion of the non-insulin-dependent net glucose uptake in each subject receiving an intravenous glucose load. Based on individual parameter estimates, the fraction of total glucose disposal independent of insulin was 72.8% ± 12.0% in the 238 ND subjects over the course of the experiment, indicating the major contribution to the whole-body glucose clearance under non-diabetic conditions. This fraction was significantly reduced to 48.8% ± 16.9% in the 30 T2D subjects, although still accounting for approximately half of the total in the T2D population based on our modeling analysis. Given the potential application of glucose effectiveness as a predictor of glucose intolerance and as a potential therapeutic target for treating diabetes, more investigations of glucose effectiveness in other disease conditions can be conducted using the hierarchical modeling framework reported herein.

Keywords: EM algorithm; glucose-insulin; insulin sensitivity; intravenous glucose tolerance test; minimal model.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Overview of covariate values and relationships. Histograms plots for continuous covariates and bar graphs for discrete covariates are shown on the diagonal. In the lower triangle, the boxplots between continuous and discrete covariates and scatter plots between continuous covariates are displayed. In the upper triangle, the correlation coefficients between continuous covariates are shown.
Figure 2
Figure 2
Goodness-of-fit plots of the base model without covariates and the final model with covariates. (A) observed glucose concentration versus population prediction from the base model. (B) observed glucose concentration versus population prediction from the final model. (C) conditional standardized residuals versus population prediction in the final model. (D) conditional standardized residual in the final model versus time. Blue lines are the lines of identity or zero value; red lines are loess smooth curves.
Figure 3
Figure 3
Violin plots showing the distribution of the individual subjects conditional mean estimates of GEZI in the ND and T2D cohorts. Boxplots were inserted for each cohort to indicate medians and interquartile ranges.
Figure 4
Figure 4
The black lines show the covariate model prediction of the typical value of SI versus BMI in ND subjects, with the solid line indicating subjects in IVGTT and dash line indicating IM-IVGTT. The red lines are the corresponding curves in T2D patients.
Figure 5
Figure 5
Violin plots showing the distribution of the fraction of non–insulin-dependent net glucose disposal in 238 ND subjects and 30 T2D patients that underwent an IVGTT test. Boxplots were inserted for each cohort to indicate medians and interquartile ranges.

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